'''simple docstring'''





import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE_:	List[Any]    =logging.getLogger()





def       lowerCAmelCase_							(   snake_case_			:					Path   ,       snake_case_			:					list     )       -> int:


     '''simple docstring'''




     UpperCAmelCase_			       =  "\n".join(snake_case_     )
     Path(snake_case_     ).open("w"     ).writelines(snake_case_     )


SCREAMING_SNAKE_CASE_:	Any    ='patrickvonplaten/t5-tiny-random'
SCREAMING_SNAKE_CASE_:	Dict    ='sshleifer/bart-tiny-random'
SCREAMING_SNAKE_CASE_:	Any    ='sshleifer/tiny-mbart'

SCREAMING_SNAKE_CASE_:	Any    =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class 			__A (					UpperCamelCase__      ):



    def    _lowercase		(self       :       str  ,     __a       :       List[str]					):
         UpperCAmelCase_			       =  Path(self.get_auto_remove_tmp_dir()					) / "utest_input.source"
         UpperCAmelCase_			       =  input_file_name.parent / "utest_output.txt"
         assert not output_file_name.exists()
         UpperCAmelCase_			       =  [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
         _dump_articles(__a  ,     __a					)

         UpperCAmelCase_			       =  str(Path(self.get_auto_remove_tmp_dir()					) / "scores.json"					)
         UpperCAmelCase_			       =  "translation_en_to_de" if model == T5_TINY else "summarization"
         UpperCAmelCase_			       =  f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

         with patch.object(__a  ,     "argv"  ,     __a					):
              run_generate()
              assert Path(__a					).exists()
              # os.remove(Path(output_file_name))



    def    _lowercase		(self       :       Union[str, Any]					):
         self.run_eval_tester(__a					)



    @parameterized.expand([BART_TINY, MBART_TINY]					)
    @slow
    def    _lowercase		(self       :       List[Any]  ,     __a       :       str					):
         self.run_eval_tester(__a					)






    @parameterized.expand([T5_TINY, MBART_TINY]					)
    @slow
    def    _lowercase		(self       :       List[str]  ,     __a       :       Optional[Any]					):
         UpperCAmelCase_			       =  Path(self.get_auto_remove_tmp_dir()					) / "utest_input.source"
         UpperCAmelCase_			       =  input_file_name.parent / "utest_output.txt"
         assert not output_file_name.exists()

         UpperCAmelCase_			       =  {
             "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
             "de": [
                 "Maschinelles Lernen ist großartig, oder?",
                 "Ich esse gerne Bananen",
                 "Morgen ist wieder ein toller Tag!",
             ],
         }

         UpperCAmelCase_			       =  Path(self.get_auto_remove_tmp_dir()					)
         UpperCAmelCase_			       =  str(tmp_dir / "scores.json"					)
         UpperCAmelCase_			       =  str(tmp_dir / "val.target"					)
         _dump_articles(__a  ,     text["en"]					)
         _dump_articles(__a  ,     text["de"]					)
         UpperCAmelCase_			       =  "translation_en_to_de" if model == T5_TINY else "summarization"
         UpperCAmelCase_			       =  f"""
            run_eval_search.py
            {model}
            {str(__a					)}
            {str(__a					)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
         testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"]					)

         with patch.object(__a  ,     "argv"  ,     __a					):
              with CaptureStdout() as cs:
                   run_search()
              UpperCAmelCase_			       =  [" num_beams | length_penalty", model, "Best score args"]
              UpperCAmelCase_			       =  ["Info"]
              if "translation" in task:
                   expected_strings.append("bleu"					)
              else:
                   expected_strings.extend(__a					)
              for w in expected_strings:
                   assert w in cs.out
              for w in un_expected_strings:
                   assert w not in cs.out
              assert Path(__a					).exists()
              os.remove(Path(__a					)					)










'''simple docstring'''







import re
import string

import numpy as np

import datasets


lowerCAmelCase_     						=				'''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''

lowerCAmelCase_     						=				'''
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    25.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    50.0


    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    75.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results["exact_match"], 1))
    100.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]
    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    33.3

'''

lowerCAmelCase_     						=				'''
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       , _KWARGS_DESCRIPTION      )
class 	_snake_case(			datasets.Metric      ):






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Dict:






    """simple docstring"""


    return datasets.MetricInfo(
        description=_DESCRIPTION    ,		citation=_CITATION    ,		inputs_description=_KWARGS_DESCRIPTION    ,		features=datasets.Features(
            {
                'predictions': datasets.Value('string'    ,		id='sequence'    ),
                'references': datasets.Value('string'    ,		id='sequence'    ),
            }    )    ,		reference_urls=[]    ,		)






 def 						_UpperCamelCase (self       :  List[str]    ,		a       :  str    ,		a       :  Tuple    ,		a       :  Tuple=None    ,		a       :  Dict=False    ,		a       :  Dict=False    ,		a       :  List[str]=False    ,		)       ->   int:






    """simple docstring"""


    if regexes_to_ignore is not None:
       for s in regexes_to_ignore:
          A__       =     np.array([re.sub(a    ,		''    ,		a    ) for x in predictions]    )
          A__       =     np.array([re.sub(a    ,		''    ,		a    ) for x in references]    )
    else:
       A__       =     np.asarray(a    )
       A__       =     np.asarray(a    )

    if ignore_case:
       A__       =     np.char.lower(a    )
       A__       =     np.char.lower(a    )

    if ignore_punctuation:
       A__       =     string.punctuation.maketrans(''    ,		''    ,		string.punctuation    )
       A__       =     np.char.translate(a    ,		table=a    )
       A__       =     np.char.translate(a    ,		table=a    )

    if ignore_numbers:
       A__       =     string.digits.maketrans(''    ,		''    ,		string.digits    )
       A__       =     np.char.translate(a    ,		table=a    )
       A__       =     np.char.translate(a    ,		table=a    )

    A__       =     predictions == references

    return {"exact_match": np.mean(a    ) * 1_00}


